A method, system and storage medium for the sinking and floating management of a lifting cage buoy

Abstract

The present invention relates to a method, system and storage medium for the sinking and floating management of a lifting cage buoy, belonging to the technical field of fishery fishing equipment. The method includes: obtaining the activity depth range of the target aquaculture biological, screening out the preliminary optimal placement depth of the lifting cage based on the activity depth range to obtain the initial placement depth; performing sinking and floating analysis and regulation on the three-dimensional simulation model of the lifting cage buoy and the three-dimensional simulation model of the inflation device based on the activity prediction model to generate the first sinking and floating regulation result; introducing the Whirlpool hash algorithm to calculate the first hash value and the second hash value, and analyzing and adjusting the first sinking and floating regulation result based on the first hash value and the second hash value to generate the second sinking and floating regulation result; obtaining the anchoring throwing range, and introducing the particle swarm algorithm to select the optimal throwing point within the anchoring throwing range. The present invention can intelligently regulate and manage the sinking and floating of the lifting net cable buoy, so that the lifting cage can meet the aquaculture requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of fishery fishing equipment, and in particular to a method, system and storage medium for managing the sinking and floating of a lifting cage buoy. Background Art

[0002] The lifting cage float is a device used in aquaculture, which is used to adjust the buoyancy of the cage to adapt to the breeding requirements of different water depths; the lifting cage is mainly composed of a breeding cage, a float, a connector, a buoyancy adjustment device and an anchoring facility, wherein the existing float is usually provided with two holes, the upper end is an air inlet hole, and the lower end is a drainage hole. When the float needs to drive the cage to sink, it is necessary to control the buoyancy adjustment device to discharge the gas in the float from the air inlet hole, so that the water enters from the drainage hole to fill the float and generate gravity, thereby driving the cage to sink; when the float needs to drive the cage to float, it is necessary to control the buoyancy adjustment device to continuously inflate the inside of the float from the air inlet hole, so that the water is discharged from the drainage hole. At this time, the mass of the float gradually decreases, thereby driving the cage to float.

[0003] However, the existing sinking and floating control methods are not accurate in controlling the aquaculture water depth of the lifting cage buoy, and there are control errors, making it difficult to accurately reach the preset aquaculture depth, making the environment at the actual aquaculture depth not suitable for the survival of the target aquatic organisms, resulting in a decrease in the survival rate of the target aquatic organisms; at the same time, the sinking and floating control of the buoy for the aquaculture depth always follows the fixed control results for output, resulting in the inability to complete intelligent control according to the aquaculture needs of the target aquatic organisms, and the sinking and floating errors of the buoy are large, affecting the aquaculture quality; and the anchoring throwing point is difficult to determine accurately, making it impossible to accurately fix the aquaculture position of the lifting cage, causing the lifting cage to deviate from the preset aquaculture area, greatly reducing the aquaculture adaptability. Summary of the invention

[0004] The present invention overcomes the shortcomings of the prior art and provides a method, system and storage medium for managing the sinking and floating of a lifting cage buoy.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] The first aspect of the present invention provides a method for managing the sinking and floating of a lifting cage buoy, comprising the following steps:

[0007] Obtaining the aquaculture demand of the lifting cage, determining the activity depth range of the target aquacultured aquatic organism in the aquatic knowledge map according to the aquaculture demand, screening the initial optimal placement depth of the lifting cage based on the activity depth range, and obtaining the initial placement depth;

[0008] Construct a three-dimensional simulation model of the lifting cage buoy and a three-dimensional simulation model of the inflation device. At the same time, construct a sinking and floating deviation dynamic prediction model, obtain the first sinking and floating deviation prediction value based on the sinking and floating deviation dynamic prediction model, and combine the first sinking and floating deviation prediction value and the initial placement depth to perform sinking and floating analysis and regulation on the three-dimensional simulation model of the lifting cage buoy and the three-dimensional simulation model of the inflation device, and generate the first sinking and floating regulation result;

[0009] Obtain the current placement depth of the lifting cage, introduce the Whirlpool hash algorithm to calculate the hash value between the initial placement depth and the current placement depth, obtain the first hash value, calculate the hash value between the initial placement depth and the second actual aquaculture depth after correction and regulation, obtain the second hash value, and analyze and adjust the first sinking and floating regulation result based on the first hash value and the second hash value to generate the second sinking and floating regulation result;

[0010] Obtain the sinking and floating deviation trajectory within a preset time period based on the sinking and floating deviation dynamic prediction model, calculate the anchoring throwing range in combination with the sinking and floating deviation trajectory and the area to be placed, introduce the particle swarm algorithm to select the best throwing point within the anchoring throwing range, and control the anchoring throwing device to accurately throw the anchor based on the coordinate values of the best throwing point.

[0011] Further, in a preferred embodiment of the present invention, the method for obtaining the aquaculture requirements of the lifting cage, determining the activity depth range of the target aquaculture aquatic organisms in the aquaculture knowledge graph according to the aquaculture requirements, and screening out the preliminary best placement depth of the lifting cage based on the activity depth range to obtain the initial placement depth specifically includes the following steps:

[0012] Obtain the aquaculture requirements of the lifting cage, extract the types of aquatic organisms to be cultured based on the aquaculture requirements of the lifting cage, and obtain the type information of the target aquaculture aquatic organisms;

[0013] Construct an aquaculture knowledge graph, define the type information of the target aquaculture aquatic organisms as the retrieval keyword, import the retrieval keyword into the aquaculture knowledge graph for retrieval, and determine the activity depth range of the target aquaculture aquatic organisms;

[0014] Obtain the area to be placed of the lifting cage, search all the placement depth records of the area to be placed in the aquaculture log of the lifting cage based on the activity depth range, obtain a number of historical placement depth data, and at the same time obtain the survival rate of the target aquaculture aquatic organisms corresponding to each historical placement depth data;

[0015] Determine whether the survival rate of the target aquaculture organisms corresponding to each historical release depth data is greater than the preset survival rate. If it is greater, extract all the survival rates of the target aquaculture organisms greater than the preset survival rate, construct a descending order list, import the extracted survival rates of the target aquaculture organisms into the descending order list for sorting. After the sorting is completed, obtain the maximum survival rate of the target aquaculture organisms;

[0016] Use the historical release depth data corresponding to the maximum survival rate of the target aquaculture organisms as the preliminary optimal release depth of the lift net cage to obtain the initial release depth.

[0017] Further, in a preferred embodiment of the present invention, construct a three-dimensional simulation model of the lift net cage buoy and an inflation device three-dimensional simulation model, and simultaneously construct a sinking and floating deviation dynamic prediction model. Based on the sinking and floating deviation dynamic prediction model, obtain the first sinking and floating deviation prediction value, and combine the first sinking and floating deviation prediction value and the initial release depth to perform sinking and floating analysis and regulation on the lift net cage buoy three-dimensional simulation model and the inflation device three-dimensional simulation model, and generate the first sinking and floating regulation result, which specifically includes the following steps:

[0018] Use a camera with three-dimensional perception function to capture the structured light images of the lift net cage buoy and the inflation device, and analyze the structured light images based on structured light technology to obtain a number of point cloud data;

[0019] Introduce the SIFT algorithm to extract the features of the point cloud data, extract the feature descriptors of each point cloud data, calculate the matching of each feature descriptor to generate a feature matching result. At the same time, introduce the RANSAC algorithm to randomly sample a group of matching point pairs in the feature matching result, calculate the point cloud registration transformation matrix according to the randomly sampled matching point pairs, and perform repeated sampling and iterative optimization on the remaining matching point pairs in the point cloud registration transformation matrix to obtain the point cloud data registration result;

[0020] Based on the voxelization method, perform voxel conversion analysis on the point cloud data registration result to obtain the lift net cage buoy three-dimensional simulation model and the inflation device three-dimensional simulation model;

[0021] Obtain a number of historical wind direction monitoring information and a number of historical water flow monitoring information, construct a sinking and floating deviation dynamic prediction model based on the number of historical wind direction monitoring information and the number of historical water flow monitoring information, and obtain the first sinking and floating deviation prediction value when the lift net cage is at the initial release depth through the activity prediction model;

[0022] Calculate the depth of the lift - type cage after being affected by the first sinking and floating offset prediction value at the initial release depth to obtain the first actual farming depth. If the first actual farming depth is higher than the initial release depth, calculate the Euclidean distance between the first actual farming depth and the initial release depth to obtain the first Euclidean distance. Based on the first Euclidean distance, simulate and control the opening of the water inlet of the lift - type cage buoy three - dimensional simulation model so that water fills the buoy and drives the lift - type cage to sink;

[0023] If the first actual farming depth is lower than the initial release depth, recalculate the Euclidean distance between the first actual farming depth and the initial release depth to obtain the second Euclidean distance. Based on the second Euclidean distance, simulate and control the opening of the air inlet of the inflating device three - dimensional simulation model so that air inflates into the buoy to drain water and drives the lift - type cage to float, and finally generate the first sinking and floating regulation result.

[0024] Further, in a preferred embodiment of the present invention, the steps of obtaining a plurality of historical wind direction monitoring information and a plurality of historical water flow monitoring information, and constructing a sinking and floating offset dynamic prediction model based on the plurality of historical wind direction monitoring information and the plurality of historical water flow monitoring information are as follows:

[0025] Preset a time - stamp interval, and based on the time - stamp interval, extract the wind direction monitoring data and water flow monitoring data in the lift - type cage farming log for the area to be released to obtain a plurality of historical wind direction monitoring information and a plurality of historical water flow monitoring information;

[0026] Introduce the ARIMA model, identify the order of the ARIMA model through the autocorrelation function and the partial autocorrelation function to obtain the order identification result, and fit and evaluate the ARIMA model through the order identification result to obtain the fitted ARIMA model;

[0027] Import the plurality of historical wind direction monitoring information and the plurality of historical water flow monitoring information into the fitted ARIMA model for dynamic prediction to obtain the sinking and floating offset dynamic prediction model.

[0028] Further, in a preferred embodiment of the present invention, the steps of obtaining the current release depth of the lift - type cage, introducing the Whirlpool hash algorithm to calculate the hash value between the initial release depth and the current release depth to obtain the first hash value, calculating the hash value between the initial release depth and the second actual farming depth after correction and regulation to obtain the second hash value, and analyzing and adjusting the first sinking and floating regulation result based on the first hash value and the second hash value to generate the second sinking and floating regulation result are as follows:

[0029] After generating the first sinking and floating control result, obtain the current deployment depth of the lift net cage. Based on the sinking and floating displacement dynamic prediction model, obtain the second sinking and floating displacement prediction value of the lift net cage at a preset timestamp, and calculate the depth of the lift net cage after being affected by the second sinking and floating displacement prediction value at the current deployment depth to obtain the second actual aquaculture depth;

[0030] Introduce the Whirlpool hashing algorithm to calculate the hash length between the initial deployment depth and the current deployment depth. Split the initial deployment depth and the current deployment depth into several data blocks, perform multiple iterations on each data block based on the Whirlpool hashing algorithm to generate round constants, and sequentially input the several data blocks into the round constants for processing to obtain the first hash value;

[0031] Calculate the distance between the second actual aquaculture depth and the initial deployment depth to obtain a deviation distance value, and perform correction control on the deviation distance value based on the first sinking and floating control result to obtain the second actual aquaculture depth after correction control;

[0032] Based on the Whirlpool hashing algorithm, calculate the hash length between the initial deployment depth and the second actual aquaculture depth after correction control to obtain the second hash value;

[0033] If the first hash value is less than the second hash value, it means that the first sinking and floating control result cannot be output as the control scheme of the lift net cage buoy for the second predicted activity depth. Calculate the deviation between the first hash value and the second hash value to obtain a deviation threshold;

[0034] Adjust the first sinking and floating control result based on the deviation threshold, and input the adjusted first sinking and floating control result into the three-dimensional simulation model of the lift net cage buoy and the three-dimensional simulation model of the inflation device to perform secondary simulation control, and finally generate the second sinking and floating control result.

[0035] Further, in a preferred embodiment of the present invention, the sinking and floating displacement trajectory within a preset time period is obtained based on the sinking and floating displacement dynamic prediction model, the anchoring throwing range is calculated by combining the sinking and floating displacement trajectory with the area to be deployed, and the particle swarm algorithm is introduced to select the best throwing point within the anchoring throwing range, and the anchoring throwing device is controlled to accurately throw the anchor based on the coordinate values of the best throwing point, which specifically includes the following steps:

[0036] Based on the sinking and floating displacement dynamic prediction model, obtain multiple sinking and floating displacement values within a preset time period, and draw a sinking and floating displacement trajectory according to the multiple sinking and floating displacement values within the preset time period;

[0037] Divide the area to be deployed of the lifting net cage into multiple sub - deployment areas, introduce the Pearson correlation coefficient to calculate the overlap degree between each sub - deployment area and the floating and sinking offset trajectory, obtain multiple overlap degrees, extract the sub - deployment areas with an overlap degree greater than the preset overlap degree, and obtain the anchoring throwing range;

[0038] Introduce the particle swarm algorithm to select throwing points in the anchoring throwing range, generate the velocities and positions of several particles in the anchoring throwing range, and synchronously update the velocities and positions based on the individual best positions and the global best positions of each particle to obtain the updated particle positions. Calculate the objective function based on the updated particle positions to obtain several fitness values;

[0039] Based on the several fitness values, update the individual best position and the global best position of each particle in real - time until the maximum number of iterations is met, and obtain the best throwing point position;

[0040] Construct a plane grid coordinate system, embed the anchoring throwing range into the plane grid coordinate system to obtain the grid coordinate values corresponding to the anchoring throwing range, determine the coordinate values of the best throwing point position based on the grid coordinate values, and upload the coordinate values of the best throwing point position to the control terminal of the anchoring throwing device, so as to accurately throw the anchor.

[0041] The second aspect of the present invention provides a floating and sinking management system for a lifting net cage float. The floating and sinking management system for a lifting net cage float includes a memory and a processor. A floating and sinking management method program for a lifting net cage float is stored in the memory. When the floating and sinking management method program for a lifting net cage float is executed by the processor, the following steps are implemented:

[0042] Obtain the aquaculture requirements of the lifting net cage, determine the activity depth range of the target aquaculture aquatic organism in the aquaculture knowledge graph according to the aquaculture requirements, and screen out the preliminary best deployment depth of the lifting net cage based on the activity depth range to obtain the initial deployment depth;

[0043] Construct a three - dimensional simulation model of the lifting net cage float and a three - dimensional simulation model of the inflating device, and at the same time construct a floating and sinking deviation dynamic prediction model. Obtain the first floating and sinking deviation prediction value based on the floating and sinking deviation dynamic prediction model, and perform floating and sinking analysis and regulation on the three - dimensional simulation model of the lifting net cage float and the three - dimensional simulation model of the inflating device in combination with the first floating and sinking deviation prediction value and the initial deployment depth to generate the first floating and sinking regulation result;

[0044] Obtain the current deployment depth of the lift net cage, introduce the Whirlpool hashing algorithm to calculate the hash value between the initial deployment depth and the current deployment depth, obtain the first hash value, calculate the hash value between the initial deployment depth and the second actual aquaculture depth after correction and regulation, obtain the second hash value, and analyze and adjust the first sinking and floating regulation result based on the first hash value and the second hash value to generate the second sinking and floating regulation result;

[0045] Obtain the sinking and floating offset trajectory within a preset time period based on the sinking and floating offset dynamic prediction model, calculate the anchoring throwing range in combination with the sinking and floating offset trajectory and the area to be deployed, introduce the particle swarm optimization algorithm to select the best throwing point within the anchoring throwing range, and control the anchoring throwing device to accurately throw the anchor based on the coordinate values of the best throwing point.

[0046] The third aspect of the present invention provides a computer-readable storage medium, which includes a program for the sinking and floating management method of a lift net cage float. When the program for the sinking and floating management method of a lift net cage float is executed by a processor, the steps of any one of the sinking and floating management methods of a lift net cage float are implemented.

[0047] The present invention solves the technical defects existing in the background art. The beneficial technical effects of the present invention are as follows:

[0048] Obtain the activity depth range of the target aquaculture aquatic organism, screen out the preliminary best deployment depth of the lift net cage based on the activity depth range to obtain the initial deployment depth; construct a three-dimensional simulation model of the lift net cage float and a three-dimensional simulation model of the inflation device, and at the same time construct an activity prediction model of the target aquatic organism, perform sinking and floating analysis and regulation on the three-dimensional simulation model of the lift net cage float and the three-dimensional simulation model of the inflation device based on the activity prediction model to generate the first sinking and floating regulation result; obtain the current deployment depth of the lift net cage, introduce the Whirlpool hashing algorithm to calculate the hash value between the current deployment depth and the initial deployment depth to obtain the first hash value, calculate the hash value between the first actual aquaculture depth and the second actual aquaculture depth to obtain the second hash value, and analyze and adjust the first sinking and floating regulation result based on the first hash value and the second hash value to generate the second sinking and floating regulation result; obtain the anchoring throwing range, introduce the particle swarm optimization algorithm to select the best throwing point within the anchoring throwing range, and control the anchoring throwing device to accurately throw the anchor based on the coordinate values of the best throwing point. The present invention can intelligently regulate and manage the sinking and floating of the lift net cage float, so that the lift net cage can meet the aquaculture needs of the target aquatic organism. Description of the Drawings

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A first method flow chart of a method for managing the sinking and floating of a lifting cage buoy is shown;

[0051] Figure 2 A second method flow chart of a method for managing the sinking and floating of a lifting cage buoy is shown;

[0052] Figure 3 A third method flow chart of a method for managing the sinking and floating of a lifting cage buoy is shown;

[0053] Figure 4 The invention shows a system framework diagram of a sinking and floating management system of a lifting cage buoy. DETAILED DESCRIPTION

[0054] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0056] The first aspect of the present invention provides a method for managing the sinking and floating of a lifting cage buoy, such as Figure 1 As shown, the following steps are included:

[0057] S102: Acquire the breeding demand of the lifting cage, determine the activity depth range of the target breeding aquatic organism in the aquatic knowledge map according to the breeding demand, and screen out the initial optimal placement depth of the lifting cage based on the activity depth range to obtain the initial placement depth;

[0058] S104: Construct a three-dimensional simulation model of the lifting cage buoy and a three-dimensional simulation model of the inflation device. Meanwhile, construct a dynamic prediction model for sinking, floating, and deviation, obtain the first predicted value of sinking and floating deviation based on the dynamic prediction model for sinking, floating, and deviation, and perform sinking and floating analysis and regulation on the three-dimensional simulation model of the lifting cage buoy and the three-dimensional simulation model of the inflation device by combining the first predicted value of sinking and floating deviation and the initial placement depth, and generate the first sinking and floating regulation result;

[0059] S106: Obtain the current placement depth of the lifting cage, introduce the Whirlpool hashing algorithm to calculate the hash value between the initial placement depth and the current placement depth to obtain the first hash value, calculate the hash value between the initial placement depth and the second actual aquaculture depth after correction and regulation to obtain the second hash value, and analyze and adjust the first sinking and floating regulation result based on the first hash value and the second hash value to generate the second sinking and floating regulation result;

[0060] S108: Obtain the sinking, floating, and deviation trajectory within a preset time period based on the dynamic prediction model for sinking, floating, and deviation, calculate the anchoring throwing range by combining the sinking, floating, and deviation trajectory and the area to be placed, introduce the particle swarm optimization algorithm to select the best throwing point within the anchoring throwing range, and control the anchoring throwing device to accurately throw the anchor based on the coordinate values of the best throwing point.

[0061] Further, in a preferred embodiment of the present invention, to obtain the aquaculture requirements of the lifting cage, determine the activity depth range of the target aquaculture aquatic organisms in the aquaculture knowledge graph according to the aquaculture requirements, and screen out the preliminary best placement depth of the lifting cage based on the activity depth range to obtain the initial placement depth, which specifically includes the following steps:

[0062] Obtain the aquaculture requirements of the lifting cage, extract the types of aquatic organisms to be cultured based on the aquaculture requirements of the lifting cage to obtain the type information of the target aquaculture aquatic organisms;

[0063] Construct an aquaculture knowledge graph, define the type information of the target aquaculture aquatic organisms as the search keyword, import the search keyword into the aquaculture knowledge graph for retrieval, and determine the activity depth range of the target aquaculture aquatic organisms;

[0064] Obtain the area to be placed of the lifting cage, search all the placement depth records of the area to be placed in the aquaculture log of the lifting cage based on the activity depth range to obtain several historical placement depth data, and simultaneously obtain the survival rate of the target aquaculture aquatic organisms corresponding to each historical placement depth data;

[0065] Determine whether the survival rate of the target aquaculture organisms corresponding to each historical release depth data is greater than the preset survival rate. If it is greater, extract all the target aquaculture organism survival rates greater than the preset survival rate, construct a descending order list, import the extracted target aquaculture organism survival rates into the descending order list for sorting. After the sorting is completed, obtain the maximum target aquaculture organism survival rate;

[0066] Take the historical release depth data corresponding to the maximum target aquaculture organism survival rate as the preliminary optimal release depth of the lift net cage to obtain the initial release depth.

[0067] It should be noted that for the aquaculture method of the lift net cage, after putting the aquaculture organism seedlings into the net cage, the net cage is put into the deep water area suitable for growth for adaptive aquaculture to achieve the purpose of improving the aquaculture rate and growth quality; for the aquaculture environment of the target aquaculture organisms, the most important thing is to determine the release depth of the lift net cage. If the wrong release depth is selected, the aquatic organisms may not be able to adapt to the environment and thus die; therefore, first, the type of aquatic organisms needs to be determined according to the aquaculture requirements, and then the activity depth range of the aquatic organism species is determined in the aquatic product knowledge graph to facilitate the further accurate calculation of the release depth of the lift net cage; for the selection of the release depth of the lift net cage, it can be screened according to the historical release depth. Since the activity depth range of the aquatic organism species may exist in the same area to be released, different historical release depths in the area to be released can be used as the screening basis; among them, each historical release depth corresponds to a different survival rate of the target aquaculture organisms. Therefore, selecting the area with the maximum survival rate for release can ensure an excellent aquaculture environment for the target aquaculture organisms and improve the aquaculture survival rate; finally, the historical release depth corresponding to the maximum survival rate is carefully output as the initial release depth to control the release of the lift net cage. The present invention can select the initial release depth of the net cage according to the aquatic organisms to be cultured in the lift net cage, thereby improving the aquaculture quality of the target aquaculture organisms, ensuring environmental adaptability, and enhancing the aquaculture survival rate.

[0068] Further, in a preferred embodiment of the present invention, the three-dimensional simulation model of the lift net cage float and the three-dimensional simulation model of the inflation device are constructed, and at the same time, a sinking and floating deviation dynamic prediction model is constructed. Based on the sinking and floating deviation dynamic prediction model, a first sinking and floating deviation prediction value is obtained. Combining the first sinking and floating deviation prediction value and the initial release depth, the sinking and floating analysis and regulation of the three-dimensional simulation model of the lift net cage float and the three-dimensional simulation model of the inflation device are carried out to generate a first sinking and floating regulation result, which specifically includes the following steps:

[0069] Take the structured light images of the lifting cage buoy and the inflating device by a camera with three-dimensional perception function, and analyze the structured light images based on the structured light technology to obtain a number of point cloud data;

[0070] Introduce the SIFT algorithm to extract features from the point cloud data, extract the feature descriptors of each point cloud data, calculate and match each feature descriptor to generate a feature matching result. At the same time, introduce the RANSAC algorithm to randomly sample a set of matching point pairs in the feature matching result, calculate the point cloud registration transformation matrix according to the randomly sampled matching point pairs, and perform repeated sampling and iterative optimization on the remaining matching point pairs in the point cloud registration transformation matrix to obtain the point cloud data registration result;

[0071] Perform voxel transformation analysis on the point cloud data registration result based on the voxelization method to obtain the three-dimensional simulation model of the lifting cage buoy and the three-dimensional simulation model of the inflating device;

[0072] Obtain a number of historical wind direction monitoring information and a number of historical water flow monitoring information, construct a sinking and floating displacement dynamic prediction model based on the number of historical wind direction monitoring information and the number of historical water flow monitoring information, and obtain the first sinking and floating displacement prediction value when the lifting cage is at the initial placement depth through the activity prediction model;

[0073] Calculate the depth of the lifting cage after being affected by the first sinking and floating displacement prediction value at the initial placement depth to obtain the first actual aquaculture depth. If the first actual aquaculture depth is higher than the initial placement depth, calculate the Euclidean distance between the first actual aquaculture depth and the initial placement depth to obtain the first Euclidean distance, and simulate and control the opening of the water inlet of the three-dimensional simulation model of the lifting cage buoy based on the first Euclidean distance to make the buoyancy inside the buoy drive the lifting cage to sink;

[0074] If the first actual aquaculture depth is lower than the initial placement depth, recalculate the Euclidean distance between the first actual aquaculture depth and the initial placement depth to obtain the second Euclidean distance, and simulate and control the opening of the air inlet of the three-dimensional simulation model of the inflating device based on the second Euclidean distance to make the buoyancy inside the buoy drive the lifting cage to float, and finally generate the first sinking and floating regulation result.

[0075] It should be noted that after obtaining the initial deployment depth, the water filling control inside the buoy can be carried out according to the initial deployment depth, so that the buoy drives the net cage to gradually sink to the initial deployment depth. However, due to the negative impacts of water flow and wind direction in the aquaculture water area, the phenomenon of vertical deviation will occur when the buoy drives the lifting net cage to sink, resulting in the inability to reach the initial deployment depth and affecting the aquaculture quality of aquatic organisms in the net cage. Therefore, it is necessary to control the sinking and floating of the buoy to regulate the net cage deployment position. If actual regulation and testing are used, operation errors are likely to occur, resulting in inaccurate regulation results. At the same time, the manual output increases, the efficiency is low, and it is time-consuming and laborious. Therefore, the simulation sinking and floating regulation can be carried out by constructing a three-dimensional simulation model, so as to improve the accuracy of sinking and floating regulation, save manual output, and improve the regulation efficiency. Among them, the simultaneous introduction of the SIFT algorithm and the RANSAC algorithm can ensure the accurate extraction and registration of features in the point cloud data, and ensure the authenticity and stability of the three-dimensional simulation model. For the negative impacts of water flow and wind direction, the offset prediction is carried out by constructing a dynamic prediction model. The predicted offset data can intuitively reflect the sinking and floating regulation amplitude of the water flow and wind direction in the area to be deployed on the net cage deployment error. If the depth of the net cage after being affected by the first sinking and floating offset prediction value is higher than the initial deployment depth, then it is necessary to carry out simultaneous deflation and water filling regulation inside the buoy, so as to make the net cage sink to repair the offset error. On the contrary, it is necessary to carry out simultaneous inflation and drainage regulation inside the buoy, so as to make the net cage float to repair the offset error. The present invention can intelligently and accurately regulate the buoy according to the offset prediction results of water flow and wind direction, so as to repair the error of the net cage deviating from the target depth and ensure that the aquatic organisms in the net cage are always in a suitable aquaculture environment.

[0076] Further, in a preferred embodiment of the present invention, the method includes obtaining a plurality of historical wind direction monitoring information and a plurality of historical water flow monitoring information, and constructing a sinking and floating offset dynamic prediction model based on the plurality of historical wind direction monitoring information and the plurality of historical water flow monitoring information. As Figure 2 shown, it specifically includes the following steps:

[0077] S202: Preset a time stamp interval, and based on the time stamp interval, extract the wind direction monitoring data and water flow monitoring data in the lifting net cage aquaculture log for the area to be deployed, so as to obtain a plurality of historical wind direction monitoring information and a plurality of historical water flow monitoring information;

[0078] S204: Introduce the ARIMA model, identify the order of the ARIMA model through the autocorrelation function and the partial autocorrelation function, obtain the order identification result, and fit and evaluate the ARIMA model through the order identification result to obtain the fitted ARIMA model;

[0079] S206: Import the several historical wind direction monitoring information and several historical water flow monitoring information into the fitted ARIMA model for dynamic prediction to obtain a dynamic prediction model for the sinking and floating displacement.

[0080] It should be noted that since the movements of the wind direction and water flow change constantly over time and belong to the dynamic movement of time series, a corresponding dynamic prediction model needs to be constructed to ensure the accuracy of the offset data prediction. For the construction of the dynamic prediction model, this method uses the ARIMA model. The ARIMA model is a type of time series model that can accurately analyze and predict the change trend of time series data. Since the fitting of the ARIMA model requires a certain amount of time series data, a preset time stamp interval needs to be set to obtain the corresponding historical wind direction monitoring information and historical water flow monitoring information to ensure the time continuity between historical data and improve the prediction accuracy of the ARIMA model for the sinking and floating offsets that may be caused by the water flow and wind direction, so as to achieve efficient, fast and accurate data dynamic prediction. The present invention can perform fitting analysis on the water flow and wind direction that affect the sinking and floating offsets of the cage, thereby constructing a model for dynamically predicting the sinking and floating offsets of the lifting cage and providing accurate dynamic prediction data for the sinking and floating control of the buoy.

[0081] Further, in a preferred embodiment of the present invention, the current placement depth of the lifting cage is obtained, the Whirlpool hashing algorithm is introduced to calculate the hash value between the initial placement depth and the current placement depth to obtain the first hash value, and the hash value between the initial placement depth and the second actual aquaculture depth after correction and adjustment is calculated to obtain the second hash value. Based on the first hash value and the second hash value, the first sinking and floating control result is analyzed and adjusted to generate the second sinking and floating control result, which specifically includes the following steps:

[0082] After generating the first sinking and floating control result, the current placement depth of the lifting cage is obtained. Based on the dynamic prediction model for the sinking and floating displacement, the second sinking and floating displacement prediction value of the lifting cage at a preset time stamp is obtained, and the depth of the lifting cage after being affected by the second sinking and floating displacement prediction value at the current placement depth is calculated to obtain the second actual aquaculture depth.

[0083] The Whirlpool hashing algorithm is introduced to calculate the hash length between the initial placement depth and the current placement depth. The initial placement depth and the current placement depth are split into several data blocks. Based on the Whirlpool hashing algorithm, each data block is iterated multiple times to generate round constants, and the several data blocks are sequentially input into the round constants for processing to obtain the first hash value.

[0084] Calculate the distance between the second actual aquaculture depth and the initial release depth to obtain a deviation distance value, and perform correction and regulation on the deviation distance value based on the first sinking and floating regulation result to obtain the second actual aquaculture depth after correction and regulation;

[0085] Calculate the hash length between the initial release depth and the second actual aquaculture depth after correction and regulation based on the Whirlpool hash algorithm to obtain a second hash value;

[0086] If the first hash value is less than the second hash value, it indicates that the first sinking and floating regulation result cannot be output as the regulation scheme for the lifting net cage buoy for the second predicted activity depth. Calculate the deviation between the first hash value and the second hash value to obtain a deviation threshold;

[0087] Adjust the first sinking and floating regulation result based on the deviation threshold, and input the adjusted first sinking and floating regulation result into the three-dimensional simulation model of the lifting net cage buoy and the three-dimensional simulation model of the inflating device to perform secondary simulation regulation, and finally generate a second sinking and floating regulation result.

[0088] It should be noted that in some traditional methods for regulating the floating and sinking of pontoons, due to the low level of intelligence, the results of the first floating and sinking regulation are often not verified. At the same time, the results of the first floating and sinking regulation are directly output as the regulation results for the next offset error of the net cage, which will cause a large increase in the regulation error of the pontoon, resulting in the net cage never being able to reach the ideal placement depth, reducing the breeding survival rate of the target aquatic organisms. Therefore, it is necessary to verify the first floating and sinking regulation result. First, obtain the current placement depth of the lifting net cage. Since the floating and sinking offset of the net cage caused by water flow and wind direction occurs at all times, the current placement depth will also shift. Therefore, it is necessary to obtain the second floating and sinking offset prediction value in the dynamic prediction model of floating and sinking offset. If you want to verify the accuracy of the first floating and sinking regulation result, then you need to calculate the error range between the current placement depth after being adjusted by the first floating and sinking regulation result and the initial placement depth, and calculate the error range between the second floating and sinking offset prediction value corrected by the first floating and sinking regulation result and the initial placement depth. By comparing the magnitude relationship of the two error ranges, you can know whether the first floating and sinking regulation result is accurate. If it is accurate, then every subsequent floating and sinking offset prediction can use the first floating and sinking regulation result to control the floating and sinking of the pontoon, saving redundant floating and sinking regulation calculation steps, saving time and effort, and ensuring the regulation accuracy rate. If it is not accurate, then the first floating and sinking regulation result can be further supplemented and adjusted by calculating the deviation threshold between the error ranges to repair the regulation error rate of the first floating and sinking regulation result. Among them, the calculation of the error range is obtained by introducing the Whirlpool hash algorithm to calculate the hash value between the data. The hash value can represent the error range between the data to a certain extent, reducing the amount of data processing and expressing clearly. The present invention can further verify the accuracy rate of the calculated first floating and sinking regulation result, save the calculation time of more redundant regulation parameters, and improve the floating and sinking regulation rate of the pontoon.

[0089] Furthermore, in a preferred embodiment of the present invention, the floating and sinking offset trajectory within a preset time period is obtained based on the dynamic prediction model of floating and sinking offset. The anchoring throwing range is calculated by combining the floating and sinking offset trajectory and the area to be placed. The particle swarm algorithm is introduced to select the best throwing point within the anchoring throwing range, and the anchoring throwing device is controlled to accurately throw the anchor based on the coordinate values of the best throwing point, as Figure 3 shown, specifically including the following steps:

[0090] S302: Obtain multiple floating and sinking offset values within a preset time period based on the dynamic prediction model of floating and sinking offset, and draw a floating and sinking offset trajectory according to the multiple floating and sinking offset values within the preset time period;

[0091] S304: Divide the area to be placed with the lift - type cage into multiple sub - placement areas, introduce the Pearson correlation coefficient to calculate the overlapping degree between each sub - placement area and the floating - sinking offset trajectory, obtain multiple overlapping degrees, extract the sub - placement areas with overlapping degrees greater than the preset overlapping degree to obtain the anchoring throwing range;

[0092] S306: Introduce the particle swarm algorithm to select throwing points within the anchoring throwing range, generate the velocities and positions of several particles within the anchoring throwing range, and synchronously update the velocities and positions based on the individual best position and the global best position of each particle to obtain the updated particle positions. Calculate the objective function based on the updated particle positions to obtain several fitness values;

[0093] S308: Based on the several fitness values, update the individual best position and the global best position of each particle in real - time until the maximum number of iterations is met to obtain the best throwing point;

[0094] S310: Construct a plane grid coordinate system, embed the anchoring throwing range into the plane grid coordinate system to obtain the grid coordinate values corresponding to the anchoring throwing range, determine the coordinate values of the best throwing point based on the grid coordinate values, and upload the coordinate values of the best throwing point to the control terminal of the anchoring throwing device, thereby precisely throwing the anchor.

[0095] It should be noted that after the lift - type cage is placed to the initial placement depth, due to the interference of water flow and wind direction, the lift - type cage will float and move, causing the cage to gradually move away from the ideal aquaculture area and depth, which is not conducive to the high - quality aquaculture of target aquatic organisms; usually, an anchor is thrown to the bottom to fix the lift - type cage to prevent the cage from floating and moving. However, under the influence of water flow and wind direction, the cage will show a certain floating - sinking offset trajectory. In order to prevent the cage from moving along the floating - sinking offset trajectory, it is necessary to accurately select the anchor - throwing point to improve the fixing performance of the anchor for the cage and prevent the cage from deviating from the ideal aquaculture area and depth; among them, the floating - sinking offset trajectory is drawn based on the area to be placed with the lift - type cage. In other words, the floating - sinking offset trajectory occurs within the area to be placed with the lift - type cage. Therefore, by calculating the overlapping degree between each sub - placement area and the floating - sinking offset trajectory based on the Pearson correlation coefficient, the approximate range where the anchor can be thrown can be known. Finally, the particle swarm algorithm is introduced to calculate the best throwing point within the approximate range where the anchor can be thrown. The particle swarm algorithm can search for the global optimal solution or a solution space close to the optimal solution, ensuring that the selected throwing point is more suitable for throwing compared to other points and improving the fixing stability of the anchor. The present invention can screen the best throwing point of the anchor so that the anchor can firmly and stably drag and fix the cage, avoiding the deviation phenomenon of the cage.

[0096] In addition, the method for managing the sinking and floating of the lifting cage buoy further includes the following steps:

[0097] Based on the big data network, obtain the remaining service life of the cage corresponding to different usage durations, construct a prediction model for the remaining service life of the cage based on the deep belief neural network, and import the remaining service life of the cage corresponding to the different usage periods into the prediction model for the remaining service life of the cage for training to obtain a trained prediction model for the remaining service life of the cage;

[0098] Obtain the actual usage duration of the current cage, and import the actual usage duration of the current cage into the trained prediction model for the remaining service life of the cage to obtain the predicted remaining service life of the current cage;

[0099] Construct a fishing gear knowledge graph, retrieve the breakage rate corresponding to the predicted remaining service life of the current cage in the fishing gear knowledge graph, and at the same time obtain the maintenance plan associated with the breakage rate;

[0100] Obtain the maintenance cost values corresponding to each maintenance plan, determine whether each maintenance cost value is greater than the preset maintenance cost value. If it is greater, then eliminate the maintenance cost value greater than the preset maintenance cost value, sort the remaining maintenance cost values from small to large, and extract the maintenance plan corresponding to the smallest maintenance cost value for output.

[0101] It should be noted that during the sinking and floating, aquaculture, and handling of the lifting cage, the cage may be damaged, resulting in a significant reduction in the service life of the lifting cage. Then, the cage needs to be replaced or repaired, increasing the output of aquaculture costs. Since the usage duration of the cage is one of the important factors determining the remaining life, a prediction model can be constructed by obtaining the remaining service life of the cage corresponding to different usage durations, and the predicted remaining service life of the current cage can be determined through the actual usage duration of the current cage, improving the matching accuracy of the cage breakage rate. The breakage rate of the cage will gradually increase as the usage duration gets longer. Therefore, the cage maintenance plans corresponding to different breakage rates are different, and the maintenance costs also vary. Therefore, it is necessary to screen the maintenance plans according to the maintenance costs, and usually select the plan with the smallest maintenance cost as the final plan for the breakage rate of the current cage. The present invention screens out a highly matching and cost-effective cage maintenance plan to repair the damage of the current cage by predicting the remaining service life of the current cage, improving the service life of the current cage and reducing the output of aquaculture and maintenance costs.

[0102] In a second aspect of the present invention, a floating box floating and sinking management system for a lift-type fish cage is provided. The floating box floating and sinking management system for a lift-type fish cage includes a memory 41 and a processor 42. A floating and sinking management method program for a lift-type fish cage is stored in the memory 41. When the floating and sinking management method program for a lift-type fish cage is executed by the processor 42, as Figure 4 shown, the following steps are implemented:

[0103] Obtain the aquaculture requirements of the lift-type fish cage, determine the activity depth range of the target aquaculture aquatic organism in the aquaculture knowledge graph according to the aquaculture requirements, and screen out the preliminary optimal placement depth of the lift-type fish cage based on the activity depth range to obtain the initial placement depth;

[0104] Construct a three-dimensional simulation model of the floating box of the lift-type fish cage and a three-dimensional simulation model of the inflation device, and at the same time construct a floating and sinking deviation dynamic prediction model. Obtain the first floating and sinking deviation prediction value based on the floating and sinking deviation dynamic prediction model, and perform floating and sinking analysis and regulation on the three-dimensional simulation model of the floating box of the lift-type fish cage and the three-dimensional simulation model of the inflation device in combination with the first floating and sinking deviation prediction value and the initial placement depth to generate the first floating and sinking regulation result;

[0105] Obtain the current placement depth of the lift-type fish cage, introduce the Whirlpool hashing algorithm to calculate the hash value between the initial placement depth and the current placement depth to obtain the first hash value, calculate the hash value between the initial placement depth and the second actual aquaculture depth after correction and regulation to obtain the second hash value, and analyze and adjust the first floating and sinking regulation result based on the first hash value and the second hash value to generate the second floating and sinking regulation result;

[0106] Obtain the floating and sinking deviation trajectory within a preset time period based on the floating and sinking deviation dynamic prediction model, calculate the anchoring throwing range in combination with the floating and sinking deviation trajectory and the area to be placed, introduce the particle swarm optimization algorithm to select the best throwing point within the anchoring throwing range, and control the anchoring throwing device to accurately throw the anchor based on the coordinate value of the best throwing point.

[0107] In a third aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a floating and sinking management method program for a lift-type fish cage. When the floating and sinking management method program for a lift-type fish cage is executed by a processor, the steps of any one of the floating and sinking management methods for a lift-type fish cage are implemented.

[0108] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for managing the sinking and floating of a lifting net cage buoy, characterized in that, The method includes the following steps: Obtain the aquaculture requirements of the lift net cage, determine the activity depth range of the target aquaculture aquatic organisms in the aquaculture knowledge graph according to the aquaculture requirements, and filter out the preliminary optimal deployment depth of the lift net cage based on the activity depth range to obtain the initial deployment depth; Construct a three-dimensional simulation model of the lift net cage buoy and a three-dimensional simulation model of the inflation device, and at the same time construct a sinking and floating deviation dynamic prediction model. Obtain the first sinking and floating deviation prediction value based on the sinking and floating deviation dynamic prediction model, and combine the first sinking and floating deviation prediction value and the initial deployment depth to perform sinking and floating analysis and regulation on the three-dimensional simulation model of the lift net cage buoy and the three-dimensional simulation model of the inflation device, and generate the first sinking and floating regulation result; Obtain the current deployment depth of the lift net cage, introduce the Whirlpool hash algorithm to calculate the hash value between the initial deployment depth and the current deployment depth to obtain the first hash value, calculate the hash value between the initial deployment depth and the second actual aquaculture depth after correction and regulation to obtain the second hash value, and analyze and adjust the first sinking and floating regulation result based on the first hash value and the second hash value to generate the second sinking and floating regulation result; Obtain the sinking and floating deviation trajectory within a preset time period based on the sinking and floating deviation dynamic prediction model, calculate the anchoring throwing range in combination with the sinking and floating deviation trajectory and the area to be deployed, introduce the particle swarm algorithm to select the best throwing point within the anchoring throwing range, and control the anchoring throwing device to accurately throw the anchor based on the coordinate values of the best throwing point.

2. The sinking and floating management method of a lifting cage buoy according to claim 1, characterized in that, The step of obtaining the aquaculture requirements of the lift net cage, determining the activity depth range of the target aquaculture aquatic organisms in the aquaculture knowledge graph according to the aquaculture requirements, and filtering out the preliminary optimal deployment depth of the lift net cage based on the activity depth range to obtain the initial deployment depth specifically includes the following steps: Obtain the aquaculture requirements of the lift net cage, and extract the types of aquatic organisms to be cultured based on the aquaculture requirements of the lift net cage to obtain the type information of the target aquaculture aquatic organisms; Construct an aquaculture knowledge graph, define the type information of the target aquaculture aquatic organisms as the search keyword, import the search keyword into the aquaculture knowledge graph for retrieval, and determine the activity depth range of the target aquaculture aquatic organisms; Obtain the area to be deployed of the lift net cage, search all the deployment depth records of the area to be deployed in the aquaculture log of the lift net cage based on the activity depth range to obtain several historical deployment depth data, and at the same time obtain the survival rate of the target aquaculture aquatic organisms corresponding to each historical deployment depth data; Judge whether the survival rate of the target aquaculture aquatic organisms corresponding to each historical deployment depth data is greater than the preset survival rate. If it is greater, extract all the survival rates of the target aquaculture aquatic organisms greater than the preset survival rate, construct a descending order list, import the extracted survival rates of the target aquaculture aquatic organisms into the descending order list for sorting, and after the sorting is completed, obtain the maximum survival rate of the target aquaculture aquatic organisms; Use the historical deployment depth data corresponding to the maximum survival rate of the target aquaculture aquatic organisms as the preliminary optimal deployment depth of the lift net cage to obtain the initial deployment depth.

3. The floating and sinking management method of a liftable cage buoy according to claim 1, characterized in that Construct a three-dimensional simulation model of the lifting cage buoy and a three-dimensional simulation model of the inflation device, and simultaneously construct a sinking and floating displacement dynamic prediction model. Based on the sinking and floating displacement dynamic prediction model, obtain the first sinking and floating deviation prediction value, and combine the first sinking and floating deviation prediction value and the initial deployment depth to perform sinking and floating analysis and regulation on the three-dimensional simulation model of the lifting cage buoy and the three-dimensional simulation model of the inflation device, and generate the first sinking and floating regulation result. The specific steps are as follows: Use a camera with three-dimensional perception function to capture the structured light images of the lifting cage buoy and the inflation device, and analyze the structured light images based on structured light technology to obtain a number of point cloud data; Introduce the SIFT algorithm to extract features from the point cloud data, extract the feature descriptors of each point cloud data, calculate the matching of each feature descriptor to generate a feature matching result. At the same time, introduce the RANSAC algorithm to randomly sample a group of matching point pairs from the feature matching result, calculate the point cloud registration transformation matrix according to the randomly sampled matching point pairs, and perform repeated sampling and iterative optimization on the remaining matching point pairs in the point cloud registration transformation matrix to obtain the point cloud data registration result; Based on the voxelization method, perform voxel transformation analysis on the point cloud data registration result to obtain a three-dimensional simulation model of the lifting cage buoy and a three-dimensional simulation model of the inflation device; Obtain a number of historical wind direction monitoring information and a number of historical water flow monitoring information, construct a sinking and floating displacement dynamic prediction model based on the number of historical wind direction monitoring information and the number of historical water flow monitoring information, and obtain the first sinking and floating displacement prediction value when the lifting cage is at the initial deployment depth through the sinking and floating displacement dynamic prediction model; Calculate the depth of the lifting cage after being affected by the first sinking and floating displacement prediction value at the initial deployment depth to obtain the first actual aquaculture depth. If the first actual aquaculture depth is higher than the initial deployment depth, calculate the Euclidean distance between the first actual aquaculture depth and the initial deployment depth to obtain the first Euclidean distance, and simulate and control the opening of the water inlet of the three-dimensional simulation model of the lifting cage buoy based on the first Euclidean distance to make the buoyancy inside the buoyancy device drive the lifting cage to sink; If the first actual aquaculture depth is lower than the initial deployment depth, recalculate the Euclidean distance between the first actual aquaculture depth and the initial deployment depth to obtain the second Euclidean distance, and simulate and control the opening of the air inlet of the three-dimensional simulation model of the inflation device based on the second Euclidean distance to make the buoyancy inside the buoyancy device drive the lifting cage to float, and finally generate the first sinking and floating regulation result.

4. The floating and sinking management method of a lifting cage buoy according to claim 3, characterized in that, The step of obtaining a number of historical wind direction monitoring information and a number of historical water flow monitoring information, and constructing a sinking and floating displacement dynamic prediction model based on the number of historical wind direction monitoring information and the number of historical water flow monitoring information specifically includes the following steps: Preset a time stamp interval, and extract the wind direction monitoring data and water flow monitoring data in the lifting cage aquaculture log based on the time stamp interval to obtain a number of historical wind direction monitoring information and a number of historical water flow monitoring information; Introduce the ARIMA model, identify the order of the ARIMA model through the autocorrelation function and the partial autocorrelation function, obtain the order identification result, and fit and evaluate the ARIMA model through the order identification result to obtain the fitted ARIMA model; Import the several historical wind direction monitoring information and several historical water flow monitoring information into the fitted ARIMA model for dynamic prediction to obtain a sinking and floating displacement dynamic prediction model.

5. The sinking and floating management method of a lifting cage buoy according to claim 1, characterized in that The method for obtaining the current placement depth of the lift net cage, introducing the Whirlpool hashing algorithm to calculate the hash value between the initial placement depth and the current placement depth to obtain the first hash value, calculating the hash value between the initial placement depth and the second actual aquaculture depth after correction and regulation, obtaining the second hash value, and analyzing and adjusting the first sinking and floating regulation result based on the first hash value and the second hash value to generate the second sinking and floating regulation result, specifically including the following steps: After generating the first sinking and floating regulation result, obtain the current placement depth of the lift net cage, obtain the second sinking and floating displacement prediction value of the lift net cage at a preset time stamp based on the sinking and floating displacement dynamic prediction model, and calculate the depth of the lift net cage after being affected by the second sinking and floating displacement prediction value at the current placement depth to obtain the second actual aquaculture depth; Introduce the Whirlpool hashing algorithm to calculate the hash length between the initial placement depth and the current placement depth, split the initial placement depth and the current placement depth into several data blocks, perform multiple iterations on each data block based on the Whirlpool hashing algorithm to generate round constants, and process the several data blocks by sequentially inputting them into the round constants to obtain the first hash value; Calculate the distance between the second actual aquaculture depth and the initial placement depth to obtain a deviation distance value, and correct and regulate the deviation distance value based on the first sinking and floating regulation result to obtain the second actual aquaculture depth after correction and regulation; Calculate the hash length between the initial placement depth and the second actual aquaculture depth after correction and regulation based on the Whirlpool hashing algorithm to obtain the second hash value; If the first hash value is less than the second hash value, it means that the first sinking and floating regulation result cannot be output as the regulation scheme of the lift net cage buoy for the second predicted activity depth, calculate the deviation between the first hash value and the second hash value to obtain a deviation threshold; Adjust the first sinking and floating regulation result based on the deviation threshold, and input the adjusted first sinking and floating regulation result into the three-dimensional simulation model of the lift net cage buoy and the three-dimensional simulation model of the inflation device to perform secondary simulation regulation, and finally generate the second sinking and floating regulation result.

6. A method for managing the sinking and floating of a lift net cage buoy according to claim 1, characterized in that The floating and sinking offset trajectory within a preset time period is obtained based on the floating and sinking offset dynamic prediction model. The anchoring throwing range is calculated by combining the floating and sinking offset trajectory with the area to be dropped. The particle swarm optimization algorithm is introduced to select the best throwing point within the anchoring throwing range, and the anchoring throwing device is controlled based on the coordinate values of the best throwing point to accurately throw the anchor. The specific steps are as follows: Based on the floating and sinking offset dynamic prediction model, multiple floating and sinking offset values within a preset time period are obtained, and the floating and sinking offset trajectory is drawn according to the multiple floating and sinking offset values within the preset time period; The area to be dropped of the lift net cage is divided into multiple sub-dropping areas. The Pearson correlation coefficient is introduced to calculate the overlapping degree between each sub-dropping area and the floating and sinking offset trajectory, obtaining multiple overlapping degrees. The sub-dropping areas with overlapping degrees greater than the preset overlapping degree are extracted to obtain the anchoring throwing range; The particle swarm optimization algorithm is introduced to select a throwing point within the anchoring throwing range. The velocities and positions of several particles are generated within the anchoring throwing range, and the velocities and positions are synchronously updated based on the individual best position and the global best position of each particle, obtaining the updated particle positions. The objective function is calculated based on the updated particle positions to obtain several fitness values; Based on the several fitness values, the individual best position and the global best position of each particle are updated in real time until the maximum number of iterations is satisfied, obtaining the best throwing point; A plane grid coordinate system is constructed, and the anchoring throwing range is embedded in the plane grid coordinate system to obtain the grid coordinate values corresponding to the anchoring throwing range. The coordinate values of the best throwing point are determined based on the grid coordinate values, and the coordinate values of the best throwing point are uploaded to the control terminal of the anchoring throwing device, so as to accurately throw the anchor.

7. A floating and sinking management system for a lifting cage buoy, characterized in that, The floating and sinking management system of a lift net cage buoy includes a memory and a processor. A floating and sinking management method program for a lift net cage buoy is stored in the memory. When the floating and sinking management method program for a lift net cage buoy is executed by the processor, the following steps are implemented: The aquaculture requirements of the lift net cage are obtained. Based on the aquaculture requirements, the activity depth range of the target aquaculture aquatic organism is determined in the aquaculture knowledge graph, and the preliminary best dropping depth of the lift net cage is screened based on the activity depth range to obtain the initial dropping depth; A three-dimensional simulation model of the lift net cage buoy and a three-dimensional simulation model of the inflation device are constructed. At the same time, a floating and sinking offset dynamic prediction model is constructed. Based on the floating and sinking offset dynamic prediction model, the first floating and sinking deviation prediction value is obtained. The three-dimensional simulation model of the lift net cage buoy and the three-dimensional simulation model of the inflation device are analyzed and regulated for floating and sinking by combining the first floating and sinking deviation prediction value and the initial dropping depth, generating the first floating and sinking regulation result; Obtain the current deployment depth of the lift net cage, introduce the Whirlpool hashing algorithm to calculate the hash value between the initial deployment depth and the current deployment depth, obtain the first hash value, calculate the hash value between the initial deployment depth and the second actual aquaculture depth after correction and adjustment, obtain the second hash value, and analyze and adjust the first sinking and floating control result based on the first hash value and the second hash value to generate the second sinking and floating control result; Obtain the sinking and floating offset trajectory within a preset time period based on the sinking and floating offset dynamic prediction model, calculate the anchoring throwing range in combination with the sinking and floating offset trajectory and the area to be deployed, introduce the particle swarm algorithm to select the best throwing point within the anchoring throwing range, and control the anchoring throwing device to accurately throw the anchor based on the coordinate values of the best throwing point.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for the sinking and floating management method of the lift net cage buoy. When the program for the sinking and floating management method of the lift net cage buoy is executed by a processor, the steps of the sinking and floating management method of the lift net cage buoy according to any one of claims 1-6 are implemented.

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